Review of Multiparameter Techniques for Precision Agriculture Using …€¦ · ·...

Journal of Network Communications and Emerging Technologies (JNCET) www.jncet.org

Volume 8, Issue 2, February (2018)

ISSN: 2395-5317 ©EverScience Publications 1

Review of Multiparameter Techniques for Precision

Agriculture Using Wireless Sensor Network

Nitin Jain 1, Sushila Madan 2, Sanjay Kumar Malik 3

1 Assistant Professor, Department of Computer Science & Engineering, Hindu College of Engineering, Sonepat 2 Associate Professor, Department of Computer Science, LSR College for Women, University of Delhi, Delhi

3 Associate Professor, Department of Computer Science & Engineering, SRM University, Delhi-NCR Campus,

Sonepat

Abstract – The main focus of Precision Agriculture is to draw out

the attention to make the availability for controlling, monitoring

and exploring the agricultural practices. Wireless Sensor Network

system, equipped with reasonable software characteristics and

hardware configuration, is a competent choice for Precision

Agriculture. For PA well-suited with WSN, an efficient and

reliable technique is strongly recommended that could preserve

energy and improve the network lifetime. Thus, a well-designed

assembly of WSN in PA can be an efficient solution for developing

countries like India. The network lifetime and energy efficiency

are the chief issues to be considered.

Index Terms – Wireless Sensor Network, Network Lifetime,

Energy Efficiency, Agriculture.

1. INTRODUCTION

1.1 Precision Agriculture

I. Mampentzidou et al. [30, 2012] stated Precision Agriculture

(PA) as the technique to decipher, to estimate and to evaluate

the condition of the crops aiming to know about the effective

use of fertilizers and the requirements to water the fields at the

time of sowing and harvesting period. PA may be defined as

the streamlined process of agriculture by applying proper

quantity of water, fertilizer, pesticide etc. at proper place in

order to enhance the yield of good quality by following ideal

conditions of the environment.

1.2 Wireless Sensor Network

I. F. Akyildiz et al. [1, 2002] described Wireless Sensor

Network (WSN) as a network composed of low-cost, low-

power and low-memory, small computational capacity, multi-

functional nodes, called sensors. These nodes are minute in

dimension. These nodes can intellect the data, process the

collected data and communicate with each other in a

collaborative manner within small displacements. These nodes

are great in number. The schematic diagram of a WSN is

depicted in figure 1.

A WSN itself is a very minute body or an infrastructure of

almost beyond concrete experience. Generally, the sensor

nodes are heavily installed intermittently in the vicinity of the

phenomenon in a manner which is not pre-determined. So the

sensor network algorithms and protocols have to exhibit self-

configuring potential. However, the sensor nodes may fail at

any stage due to certain reasons. Also, the sensor network alters

its topology very often. The sensor nodes do not enclose

universal credentials due to their huge number and bulk

overhead involved. The sensor nodes can be employed to sense

the locations constantly, detect the events, identify the events

and perform confined control of actuators.

Figure 1: Wireless Sensor Network

1.3 Precision Agriculture using Wireless Sensor Networks

The rudiments in deployment of Wireless Sensor Networks for

Precision Agriculture are discussed by I. Mampentzidou et al.

[30, 2012]. The main components existing in utility of WSN

systems, such as node platforms, operating systems, power

supply, etc. are reviewed. Before applying the practice of

WSNs in agriculture especially in crop monitoring, certain

guidelines are proposed. In PA, the scenario should be clear for

the deployment of WSN in a Greenhouse or in an open space.

One should be clearheaded about the monitoring of the specific

crop. There must be a precise summary of the crop that is to be

observed. The most common decisive agricultural parameters

are soil moisture, soil temperature, water retention capacity,

salinity, leave photosynthesis, leave temperature, leave

moisture, wetness, weather humidity, atmospheric pressure,

wind speed and wind direction etc. The choice of the kind of




sensors for exploitation depends on the precision, resolution,

communication range, energy utilization, meticulousness and

cost etc.

By and large, PA in consonant with WSNs will play a vivacious

role and contribute to improve the growth of the crop and

therefore the yield. The repercussion of the above mentioned

technology if deployed in an efficient manner will enhance the

revenue of the country, the remunerating capacity and the

general health of the common man. In a nutshell, this particular

technology will come to the expectations of the peasantry and

will progress by leaps in the future.

2. LITERATURE REVIEW

“P. C. Robert [2, 2002] discussed about the emergence of

Precision Agriculture in the mid-1980s, using newly available

technologies, to improve the application of fertilizers by

varying rates and amalgams as needed within fields. It offers a

variety of potential benefits in profitability, productivity,

sustainability, crop quality, food safety, environmental

protection, on-farm quality of life, and rural economic

development.

G. Mercier et al. [3, 2005] suggested developing a technique

that allows high or low variations detected at a regional scale

to be assessed from the two sensors: SPOT VEGETATION and

SPOT High Resolution Visible InfraRed (HRVIR) images. In

this study, the link between the images from is achieved from

the design of an artificial neural network method based on

Kohonen self-organizing map. N. Wang et al. [4, 2006]

presented an outline on the development of WSN technology

and its use in agriculture and food industry. The merits and

demerits regarding fast adoption are also discussed.

The efficiency of a proposed topology to build a WSN, based

on measuring the electrical conductivity of the field, is

discussed by K. Konstantinos et al. [5, 2007]. The work tells

how electrical conductivity influences the decision to build the

WSN topology. T. L. Dinh et al. [6, 2007] stressed the WSN

deployment to monitor the real time water quality

measurements together with the amount of water being pumped

out in the area, and to investigate the impacts of current

irrigation practice on the environment, in particular

underground water salination.

Y. Kim et al. [7, 2008] recommended the design and

instrumentation of variable rate irrigation, a WSN and software

for real-time in-field sensing and control of a site specific

precision linear move irrigation system. The communication

signals from the sensor network and irrigation controller to the

base station are successfully interfaced using low-cost

Bluetooth wireless radio communication. For PA, A.

Dwarakanath et al. [8, 2008] stressed the applicability of WSNs

where real time data for meteorology and atmospheric

possessions are sensed and imparted to the sink. The real time

deployment of WSN based greenhouse management is

recommended by X. Li et al. [9, 2008] to realize the

modernization in agriculture. The system is capable to monitor

the nature of greenhouse atmosphere, to control the

equipments, and to provide better services to customers with

compact devices.

K. H. Kwong et al. [10, 2009] opined on the application of

WSNs for monitoring the livestock and the issues regarding

hardware realization. The main purpose of the study is to

employ reasonable and efficient sensor nodes having the

capability of providing real-time communication at cheaper

hardware cost. L. Bencini et al. [11, 2009] recommended the

utility of a practical case study, starting from a real problem

and reaching the best architectural solution, with particular

focus on the hardware implementation and communication

protocol design. J. Zhang et al. [12, 2009] discussed the

classification of the existing routing protocols for WSNs. The

comparison and analysis research are also provided on route

structure, energy consumption, robustness, expansibility, node

mobility, data fusion technology, whether QoS is supported

and maximizing the network lifecycle of all kinds of routing

protocols.

An experimental agro-meteorological sensor based wireless

distributed network is described by P. Mariño et al. [13, 2010].

The purpose is to provide real measurements to validate the

different biological models used for viticulture applications. H.

Sahota et al. [14, 2010] recommended the utility of MAC and

Network layers for a WSN deployed for PA application as it

needs the collection of sensor readings periodically from pre-

set locations in a field. The design of an agricultural

environmental monitoring system with sensor technology is

proposed by L. Xiao and L. Guo [15, 2010]. The system is

capable to monitor the environmental conditions such as

temperature, humidity, and light intensity pertaining to

agriculture. An intelligent water-saving system based on

ZigBee WSN for agricultural irrigation strategy is proposed by

Z. Yao et al. [16, 2010]. It provides science basis for using

water resources under the technologies of soil moisture

sensors, air temperature sensors, precise irrigation equipments,

intelligent controller, and computer-controlled devices.

G. H. E. L. D. Lima et al. [17, 2010] focused the research on

the integration of existing computer tools in order to establish

an application development environment for WSN, uniting the

robustness of programming languages with the usability of a

friendly interface. The case study encompasses the developed

simulation environment applied to the irrigation of soccer

fields. Z. Li et al. [18, 2010] discussed the use of 2.4GHz

wireless sensor modules to set up a test platform to evaluate the

data transmission performance. Indexed packets transmitted

from a module are received by another equal module to

calculate packet delivery rate (PDR). Canopy height,

transmitter height, receiver height and transmitter-to-receiver

distance are considered as impact factors on data transmission.




The results indicated that, with the increase of the plant height,

path-loss and PDR became more correlated with each other.

R. A. Santos et al. [19, 2011] evaluated a new WSN platform

and its application in PA. To validate the technological

platform and the embedded operating system, hierarchical and

flat routing strategies are tested in a small-scale network

applied to a watermelon field. This incorporates a modified

version of a wireless location-based routing algorithm that uses

cluster-based flooding. The modified algorithm includes a

metric to monitor residual battery energy. The results show that

the new version functions better with the flat algorithm in a

small-scale agricultural network. A. U. Rehman et al. [20,

2011] demonstrated the work to reassess the requirement and

relevance of WSN technology in various phases of agricultural

domain and to report on existing system structures.

Y. D. Kim et al. [21, 2011] proposed a new WSN architecture

with autonomous robots based on beacon mode for real time

agriculture monitoring system. For the large scale multi-sensor

processing, the proposed system accomplished the intelligent

database, which generates alert messages to the handheld

terminal by means of the sensor data. S. E. Díaz et al. [22, 2011]

discussed the methodology consisting of a set of well-defined

phases that cover the complete life cycle of WSN applications

for agricultural monitoring. J. Xia et al. [23, 2011]

recommended the design by deploying a monitoring system

based on WSN for PA in a red bayberry greenhouse especially

in a hilly area. It automatically collects the data - temperature,

humidity, illumination, voltage and other parameters and

transfers the data to the remote server through GPRS.

S. Li et al. [24, 2011] suggested the design of a WSN for PAMS

(Precision Agriculture Monitor System) to monitor the

atmospheric conditions of the crops. P. Patil et al. [25, 2011]

suggested an automatic irrigation monitoring system for PA. It

regulates the required moisture level in agricultural soil. It also

proves useful in both dry and heavy rainfall conditions. Y. Jiber

et al. [26, 2011] suggested the iFarm framework to monitor the

extensive agriculture over a vast area, to reduce the infertile

land with better management of water in order to improve the

paying capacity of farmers by providing the awareness time to

time and planning the crop yields. X. Liu et al. [27, 2011]

recommended the use of a new Efficient and Portable

Reprogramming Method (EPRM) by using pre-linked native

code to reduce memory footprint and by the way to reduce the

energy consumption. To validate this new approach, EPRM is

implemented and its efficiency in terms of resource

consumption (memory and energy) is evaluated. The obtained

results show that EPRM is well adapted to high resource-

constraint wireless sensor nodes.

A fuzzy decision-making method of irrigation amount based on

Evapo-Transpiration (ET) and Soil Water Potential (SWP) is

designed by Z. Wei et al. [28, 2011]. The WSN is used to

transmit the irrigation control signal including the SWP signal

and the relative humidity, air temperature, solar radiation, wind

velocity etc. The test results show that the method and hence

the system have economic advantages with high

communication reliability and control accuracy. L. Karim et al.

[29, 2011] proposed Energy Efficient Zone-based Routing

Protocol (EEZRP) that works by deploying a minimum number

of sensor nodes for agricultural monitoring in developing

countries. Experimental results show that EEZRP protocol

outperforms the existing LEACH and DSC protocols in terms

of network lifetime.

M. K. Gupta et al. [31, 2012] demonstrated the growth of

tomato seedlings under greenhouse conditions. The

microenvironment of the seedlings (temperature and solar

radiation) is correlated with the seedling growth factors. Seed

emergence is studied for five different temperatures, and a

mathematical model is developed to fit the data to determine

relationship between seed germination and soilless medium

temperature. In post-emergence phase, a model is developed to

estimate the dry weight attained by the seedlings as a function

of cumulative heat units and cumulative solar radiation. N.

Rajput et al. [32, 2012] submitted the report after surveying the

land area used for apple farming. The work also discussed

several obstacles encountered using WSNs for apple farming.

A. Mittal et al. [33, 2012] proposed mKRISHI platform for

WSN for deprived farmers for modern agricultural

applications. The proposed framework deployed over a period

of two months shows very good results. F. Philipp et al. [34,

2012] proposed an adaptive sensor node system combining a

flexible hardware prototype and innovative energy harvesting

technique to optimise the performance of the network operating

in a large farming environment.

A state-of-art technology with solutions pertaining to PA is

demonstrated by J. M. B. Ordinas et al. [35, 2013]. The

applications and experiences of the work enlighten how WSNs

can be used for agricultural purposes. B. Singh et al. [36, 2013]

recommended the fertilizer nitrogen application to irrigated

wheat in two split doses at planting and at crown root initiation

stages of the crop. The wheat grain yield at maturity is

determined by the level of greenness of leaves at MT stage -

whether measured by a chlorophyll (SPAD) meter or an optical

sensor (GreenSeeker). J. Jao et al. [37, 2013] suggested the use

of sensor technology to collect the moisture content in the soil.

The work enlightens the comprehensive design to package the

sensor nodes for external development and shows the collected

data to validate the design. The results are obtained by using

sand soils with different water conditions.

P. Tokekar et al. [38, 2013] discussed the problem of

coordinating an Unmanned Aerial Vehicle (UAV) and an

Unmanned Ground Vehicle (UGV) for a PA application. In this

work, the ground and aerial measurements are used for

estimating Nitrogen (N) levels on-demand across a farm. The

goal is to estimate the N map over a field and classify each




point based on N deficiency levels. These estimates in turn

guide fertilizer application. The constant-factor approximation

algorithms are used. Finally, utility of the system is

demonstrated with appropriate simulations. E. Kampianakis et

al. [39, 2013] proposed the design of a novel WSN consisting

of low-power and low cost sensor nodes, deployed in a bi-static

architecture and achieving long-range backscatter

communication. The backscatter sensor network performs

environmental monitoring over a relatively wide area. A proof-

of-concept prototype WSN application is developed for

capacitive relative humidity sensing.

G. Ngandu et al. [40, 2013] performed the experiments to

determine the effect of surrounding vegetation on the wireless

signal in terms of link reliability, and signal strength for three

different types of agricultural crops, namely, ground foliage,

medium height and density vegetation, and very dense types of

foliage is analysed and discussed. The objective is to

demonstrate that current radio propagation foliage loss models

are not optimised for use in PA. X. Dong et al. [41, 2013]

suggested the use of a proof-of-concept towards an

autonomous precision irrigation system provided through the

integration of a centre pivot irrigation system with Wireless

Underground Sensor Networks (WUSNs). The results

highlight that the wireless communication channel between

soil and air is significantly affected by many spatio-temporal

aspects, such as the location and burial depth of the sensors,

soil texture and physical properties, soil moisture, and the

vegetation canopy height.

A. L. Diedrichs et al. [42, 2014] presented the development of

a low-cost and low-power WSN based on IEEE-802.15.4, for

frost characterization in PA by measuring temperature. The key

objective is to reduce the power consumption of the network.

The design and development of an agricultural controlling

system using WSN is recommended by B. B. Bhanu et al. [43,

2014] to enhance the yield and to improve the quality of

farming without observing it for all the time manually. M. R.

M. Kassim et al. [44, 2014] considered WSNs as the superlative

way to solve the agricultural problems pertaining farming

resources, decision making support, and for monitoring the

land. The proposed work focused on the software practice

management, metalwork structural design and set-up style of

the system.

S. R. Nandurkar et al. [45, 2014] presented the design to

provide simple, easy-to-operate, credible and reliable

technology within the reach of every farmer to conserve the

ground water resources. K. K. Khedo et al. [46, 2014] proposed

the implementation of a PA application, PotatoSense, for

monitoring a potato plantation field in Mauritius. Different

energy efficient algorithms are used to ensure that the system

lifespan is prolonged. Additionally, a monitoring application is

developed to process the data obtained from the simulated

WSN. The monitoring application is able to indicate the

regions of the potato field that need irrigation, pesticides or

fertilizers. I. Mat et al. [47, 2014] presented a practical, low-

cost and environmental-friendly Greenhouse Monitoring

System based on WSN technology, to monitor the key

environmental parameters such as the temperature, humidity

and soil moisture.

M. Biglarbegian and F. A. Turjman [48, 2014] discussed the

strategy of Cognitive Path Planning for mobile Data Collectors

in WSNs to efficiently collect the sensed data in a PA

application. Energy consumption is the major attribute that

impacts the performance of the proposed approach. W. Yitong

et al. [49, 2014] presented the design of a multi-parameter

wireless monitoring system. The experiment proved that the

system could completely meet the daily PA for crop growth

environment and the requirements of monitoring on the soil

composition content, and the system had high reliability. C.

Wang et al. [50, 2014] described an RFID sub-soil system that

is capable of hosting a range of sensors and communicating

their measurements wirelessly to farming vehicles. The work

describes the implementation of a prototype sensor node and

the implementation of the RFID reader using National

Instruments PXI RF modules controlled using LabVIEW.

T. Nakanishi [51, 2014] suggested the use of a generative WSN

framework that can satisfy the requirements and constraints of

individual farmers within a prescribed scope. H. Sahota and R.

Kumar [52 and 53, 2014] presented the network based

localization of sensors, placed in multiple media, based on the

measurements of received signal strength and time of arrival.

The localization problem is formulated with the goal of

parameter estimation of the joint distribution of the ranging

measurements. The study presented a sensitivity analysis of the

estimates with respect to the soil complex permittivity and

magnetic permeability.

M. H. Anisi et al. [54, 2015] classified and described the state-

of-art of WSNs and analyse the respective energy

consumptions based on their power sources. WSN approaches

in PA are categorized and discussed according to their features.

C. T. Kone et al. [55, 2015] proposed to properly tune IEEE

802.15.4 MAC parameters and the sampling frequency of

deployed sensor nodes. An analytical model of network

performance is derived and used to perform the tuning of these

trade-off parameters. Simulation analysis shows that the

scheme provides an efficient increase of sampling frequency of

sensor nodes while satisfying application requirements. A. R.

D. L. Concepción et al. [56, 2015] proposed an innovative

solution for the realization of WSNs suitable to characterize the

microclimatic behaviour in agriculture fields. The nodes

equipped with sensing units to measure several physical

parameters, are designed in order to minimize power

consumption.

E. L. Pontes et al. [57, 2015] suggested analysing some

methods to locate and increase the lifetime of a sensor network




through its potential application in PA. V. T. Varghese et al.

[58, 2015] presented a survey of WSN systems deployed for

the agricultural domain. Keeping in mind, the working

methodologies and the drawbacks of existing systems, a new

WSN approach for estimating crop yield is proposed.

N. Chen et al. [59, 2015] discussed the overview of an

integrated geospatial sensor web for agricultural soil moisture

monitoring in China. The satellite sensors, in-situ sensors, and

WSNs are firstly coupled by sensor web based cyber

infrastructure. Then multisensory collaboration methods are

designed to make full use of these different monitoring

capabilities. In addition, web processing service is utilized to

achieve online soil moisture estimating, fusing, and mapping.

Finally, an integrated online service platform is constructed to

query and display all soil moisture related resources. The

experiments conducted in Hubei province demonstrated that

this region’s agricultural soil moisture monitoring capability is

improved in terms of spatial temporal continuity and accuracy.

M. Joordens et al. [60, 2015] suggested the utility of a fully

autonomous aerial and ground system that would provide

efficient and cost effective retrieval of soil and vegetation data

for use in PA. The aerial system will survey the site and collect

spectral imagery to analyse plant density, stress and nutrition.

The ground sensors will collect soil moisture content readings

throughout the site. The data from both systems will be collated

at a central base station. The base station will also provide

housing and interface with the aerial system.

J. A. Jiang et al. [61, 2016] proposed a WSN based controlling

platform with dynamic converge-cast tree topology based

approach for precision agriculture management in orchid

greenhouses for reliable data acquisition. This comprises a

springy scheduling based scheme for the MAC protocol to

attain greater communication reliability. A ubiquitous WSN

framework using Internet technologies and Smart Object

Communication Patterns was demonstrated by F. J. F. Pastor et

al. [62, 2016] which proved to be beneficial in terms of cost

and power consumption.”

Table 1. Summarized Table of Existing Techniques for Precision Agriculture using Wireless Sensor Networks

Reference

Author(s)

Technique

Used Advantages Limitations

[3, 2005] G. Mercier

et al.

Multi-Source

Multi-

Resolution

Image Fusion by

Covering Soils

with Vegetation

-Reduces water pollution

-Restricts the pollutant fluxes to

aquatic systems

-Protects soils from raindrop

impact and splash

-Allows excess surface water to

infiltrate

-Consumes some of the

excessive nitrogen in soils

-Limits the location accuracy of

the land cover classes

[5, 2007] K.

Konstantinos

et al.

Topology

Optimization

based on

Measuring the

Field’s

Electrical

Conductivity

-Finds the optimal sensor

topology

-Lack of electrical conductivity

measurement locally

-Absence of evaluation of the

performance of using a mixture of

network topologies

[6, 2007] T. L. Dinh

et al.

Underground

Water Content

and Saline Level

-Monitors the quality of water -Saltwater intrusion should be

minimised

-Network connectivity is to be

improved

-Network outage needs to be

handled efficiently

[7, 2008] Y. Kim

et al.

Variable Rate

Watering

-Enhances application

efficiencies

-Reduces environmental

impacts

-Provides stable wireless

connectivity

-Requires to support the

assimilation of efficient watering

mechanism and effective synthesis

of sensors




[8, 2008] A.

Dwarakanath

et al.

Controlling

Irrigation and

Fertigation by

Controlling

Agricultural

Parameters

-Optimizes the network topology

-Communication efficiency

-Supports only static hierarchical

architecture

-Not suitable for high mobility

environments

[10, 2009] K. H. Kwong

et al.

Cattle

Observing

-Real time health monitoring -Needs improvement in the field of

visceral prosperity and operating

proficiency

[11, 2009] L. Bencini

et al.

Monitoring

Physiology and

Pathogens

Control

-Prevents plant vine diseases

-Vineyard water management

-Pest management

-Reliable

-Needs to be extended to support

decision making mechanism by

implementing Decision Support

System (DSS)

[12, 2009] J. Zhang et al. Dynamic

Clustering

-Organizational management of

nodes

-Network scalability

-Leads to high transmission power

and therefore high or uneven

energy consumption

-Reduces load balance degree of

the network

-Influences lifetime of the network

[13, 2010] P. Mariño

et al.

Instrumentation

of Biological

Models

-Risk prediction

-Disease assessment

-Fails to detect other threatening

pests

-Crop tracking needs to be

upgraded

[14, 2010] H. Sahota

et al.

Designing MAC

and Network

Layers using A

New Wake-Up

Synchronization

-Saves energy during wake-up

synchronization

-Balances load balance degree of

network

-Minimizes the energy

consumption

-Fails in fault detection and fault

mitigation

[15, 2010] L. Xiao and

L. Guo

Self-Motivated

Clustering

-Energy efficiency

-Improves network longevity

-Not robust in terms of packet

delivery

-Requires an Event-Condition-

Action framework

[16, 2010] Z. Yao et al. Monitoring

Water Level

-Optimal scheduling

-Improves efficiency

-Real time deployment has not

been performed

[18, 2010] Z. Li et al. Correlation

Analysis on

Impact Factors

of Data

Transmission

-Evaluates the data transmission

performance

-Deals only with the path loss as

QoS indicator

-Absence of evaluation of other

QoS factors such as packet

delivery rate and spatial resolution

[19, 2011] R. A. Santos et

al.

Localization of

Nodes with

Cluster-based

Flooding

-Monitors residual battery

energy

-Lack of impacts posed by QoS

parameters

-Deals with limited power

management

[21, 2011] Y. D. Kim

et al.

Bonfire Custom

founded

Designing Self-

Directed

Automatons

-Energy efficiency

-Reliable

-Handles unanticipated failures

in communication links

-Non-existence of effects posed by

QoS parameters




-Ensures recovery of

transmission path

[23, 2011]

J. Xia et al. Monitoring

Various

Agronomic

Parameters

-Network scalability

-Stable

-Delivers accurate service

-Energy preservation

-Needs to synchronize with respect

to time

-Requires the concept of

localization of nodes

[26, 2011] Y. Jiber et al. Decision

Support System

for Land

Monitoring

-Improves land productivity

-Optimizes the use of resources

-Efficient watering mechanism

-Controls disease

-Predicts and plans yield of the

crop

-Considers socio-economic

factors

-Fails to reveal the agronomical

and technological factors

[27, 2011] X. Liu et al. Efficient and

Portable

Reprogramming

Mode

-Reduces memory footprint

-Reduces energy consumption

-Efficient consumption of

resources

-Implementation effectiveness

-Inapplicable in heterogeneous

environments

[28, 2011] Z. Wei et al. Determining

Water Volume

by Computing

Evapo-

Transpiration

and Water

Retaining

Capacity of Soil

-High communication reliability

-Accuracy control

-Lacks in accuracy of the

imprecise computations

[29, 2011] L. Karim et al. Zone-based

Routing

Localization

-Energy efficiency

-Extends network lifetime

-Fault tolerance

-Lack of link recovery mechanism

-Requires Decision Support

System (DSS)

[33, 2012] A. Mittal et al. Received Signal

Strength

Indication

-Improves communication range

-Energy efficiency

-Enhances the overall network

lifetime

-Reduces the maintenance

overhead

-Issues related to throughput,

scalability, latency and security

are to be resolved

[34, 2012] F. Philipp

et al.

Hybrid

Heterogeneous

Energy

Harvesting

-Tracks maximum power points -Inflexible in hardware/software

interfaces

-Inefficient in terms of energy

consumption and robustness

-Overall performance of the

network is to be optimised

[36, 2013] B. Singh et al. Supplementing

Fertilizer

Nitrogen

Application

-Improves Nitrogen nutrition

-Predicts fertilizer Nitrogen

requirements

-Inappropriate recommendations

for maintaining high yields

[38, 2013] P. Tokekar

et al.

Planning for a

Symbiotic UAV

and UGV

Coordination

-Power efficiency

-Energy management

-Flops to cover the factors like

autonomous landing and soil

sampling




[39, 2013] E.

Kampianakis

et al.

Prolonging

Communication

Ranges by

Frequency

Modulation

-Accuracy control

-Better communication range

-Promotes scalability

-Lower power consumption


been performed

[40, 2013] G. Ngandu

et al.

Determining

the Effect of

Surrounding

Vegetation

-Evaluates how the consistency

of communication link varies

through vegetation

-Deals with static nodes only

-Inappropriate to provide highly

reliable communication

[41, 2013] X. Dong et al. Centre Pivot

Irrigation

Scheme

-Considers the spatio-temporal

aspects

-Fails to reveal how soils have

influences on quality of

communication and scalability of

network

-Requires error control scheme in

transmission of data packets

-Energy efficiency needs to be

improved

[42, 2014] A. L.

Diedrichs

et al.

Frost

Monitoring

-Enhances network lifetime -Energy harvesting module is

needed

-Not scalable

-Lack of frost prediction

mechanism

[45, 2014] S. R.

Nandurkar

et al.

Automatic Drip

Irrigation

-Water conservation

-Enhances yield quality

-Saves water and electricity

-Needs to minimize the effects of

fertilizers on the value of soil

moisture

[46, 2014] K. K. Khedo et

al.

Clustering using

Hybrid Energy

Efficiency

Distribution

Mechanism

-Energy efficiency

-Ensures better network lifespan

-Able to indicate the regions

which need irrigation, pesticides

and fertilizers

-Lessens the loss of data packets

while transmission

-Needs evaluation of the system in

practical field

[48, 2014] M.

Biglarbegian

and

F. A. Turjman

Cognitive Path

Planning

Strategy for

Mobile Data

Collectors

-Efficient energy consumption

-Prolongs network lifetime

-Deficiency of dynamic

environments

[49, 2014] W. Yitong

et al.

Designing

Agro-

Parameters

-Highly reliable

-High applicability

communication link

-Easy to extend

-Imprecise measurement results

are obtained

[50, 2014] C. Wang et al. Plough-able

RFID Sub-Soil

Classification

-Power harvesting efficiency

-Better power consumption

-Performance of the backscatter

wireless communication link


been performed




[51, 2014] T. Nakanishi Using a

Paradigm of

Software

Product Line

-Optimization of system

composition

-Energy harvesting functionality

-Needs evaluation of the

framework in real scenario

[52 and 53,

2014]

H. Sahota and

R. Kumar

Network

based

Localization

-Received signal strengths based

estimates offer a cheaper

solution but not very accurate

-Time of arrival measurements

offer more costly but accurate

solution

-Extensive tests are needed to

validate and calibrate the

performance evaluation models

-Accurate modelling is required

for multi-path fading and path loss

effects

[55, 2015] C. T. Kone

et al.

Tuning MAC

Parameters

-Extends network lifetime

-Reliable


been performed

[56, 2015] A. R. D. L.

Concepción

et al.

Distributed

Microclimatic

Diffused

Monitoring

-Low power consumption -Lack of support for multimedia

sensing

[57, 2015] E. L. Pontes

et al.

Node

Localization

-Balances power consumption

-Extends network longevity


been performed

[59, 2015] N. Chen et al. Soil Moisture

Geospatial Web

Processing

Service

-Improves spatio-temporal

continuity and accuracy

-Requires to support the

assimilation of efficient watering

mechanism and effective synthesis

of sensors

[60, 2015] M. Joordens

et al.

Covering Soils

with Vegetation

for Fully

Autonomous

Aerial and

Ground

Structure

-Provides efficient retrieval of

soil and vegetation data

-Conformable for unmanned

ground vehicle

-In-optimal specifications

[61, 2016] J. A. Jiang et

al.

Cultivation

Management

with Dynamic

Converge-Cast

Tree Topology

based Approach

-Reliable data acquisition

-High transmission reliability

-Lack of decision support system

-In-optimal cultivation scheduling

[62, 2016] F. J. F. Pastor

et al.

Ubiquitous

Platform using

Internet of

Things and

Smart Object

Communication

Patterns

-Cost effective

-Low power consumption

-Less water depletion

-Better data aggregation service

-Does not deal with energy

consumption of embedded devices

using harvesting resources

-Needs application based on expert

system




Reference Characteristic(s)/ Specification(s) Description(s)

[3, 2005] Routing Protocol/Algorithm ANN Kohonen Self Organizing map

Technical Contribution Multi-Source Multi-Resolution Image Fusion

Installation Area Covered (size) 1210 ha

Factors considered Spatial Resolution & Spectral Resolution

Types of Sensors

High Resolution Visible & Infrared Sensor (HRVIR)

and Vegetation Sensor

[5, 2007] Topology/ Architecture Grid & Optimised Veris topology

Node Platform Mica

Routing Protocol/Algorithm Logical Grid Routing Algorithm

Node OS TinyOS

Language nesC

Communication Network RF technology

Installation: Area Covered (size) 50 acres

Types of Sensors Soil Sensor

Sensed Parameters Moisture, Conductivity

Performance Metrics Latency, Throughput, Success/Loss rate, Energy

Consumption/ Efficiency

Factor considered Field’s Electrical Conductivity

Tool used MATLAB

Application used RMASE (Routing Modeling Application Simulation

Environment)

[6, 2007] Topology/ Architecture Highly Dynamic Network topology

(Single hop or Multi hop) Multi hop

Microcontroller / Processor Atmel ATmega128

Node Platform Fleck3

Node OS TinyOS

Radio tranceiver NRF905

Number of nodes 8

Factor considered Field’s Electrical Conductivity

Types of Sensors Electrical Conductivity sensor, PS100 Pressure sensor,

Analog sensor , Soil sensor (Salinity, Flow/Water

Volume Sensor, Flow Rate sensor)

Routing Protocol Surge_Reliable (state-of-the art sensor network

Routing Protocol)

[7, 2008] Communication Network RF technology Bluetooth

Spread Spectrum Radio Technology

Number of nodes 6

Sensing Measurements 15 minutes

Power supply Solar

Types of Sensors: Sensed Parameters Soil sensor (Temperature, Moisture, Conductivity,

Volumetric Water content) & Weather sensor (Air

Temperature, Relative Humidity, Wind speed, Wind

direction, Solar radiation precipitation i.e. Total Flux &

Flux density

Software used WISC (Wireless Infield Sensing & Control Software)

Programming Framework Microsoft Visual C++.Net

[8, 2008] Topology/ Architecture Hierarchical Network Topology (Tree-based Routing

scheme)

Routing Protocol/Algorithm Static Addressing algorithm




Installation: Area Covered (size) 26.7 ha

Types of Sensors: Sensed Parameters Soil sensor (Temperature, Moisture, Electrical

Conductivity, pH)

Paradigm used IOT (Internet of Things)

Communication Network LR-WPAN (Low rate Wireless Personal Area

Network)

[10, 2009] Topology/ Architecture Multi hop (Solar powered Relay Router)

Node Platform Mica2 & Micaz

Microcontroller / Processor Atmel ATmega128

Power supply Solar

[11, 2009] Monitoring System Laptop

Topology/ Architecture Multi hop


Types of Sensors: Sensed Parameters

Soil (Moisture)

Routing Protocol TCP/IP standard Routing Protocol

STAR (Synchronous Transmission Asynchronous

Reception)

LEPS (Link Estimation Parent Selection)

Node Platform MIDRA mote

[12, 2009] Number of nodes 100

Installation: Area Covered (size) 100X100 m2

Routing Protocol Cluster-based LEACH Protocol (Dynamic Clustering)

Topology/ Architecture Hierarchical Routing theory

Technology used RF/ZigBee

Tool used NS2

[13, 2010] Location of Monitoring Vineyard

Power supply Solar


Leave Wetness sensor, Relative Weather Humidity

sensor, Wind Speed sensor, Wind Direction sensor,

Solar Radiation Precipitation sensor

Routing Protocol Network time Protocol, Polling Protocol


[14, 2010] Monitoring System LCD/LED

Topology/ Architecture Multi hop

Microcontroller / Processor 8051 MCU

Technology used RF

Tool used TOSSIM

[15, 2010] Monitoring System LCD

Node OS TinyOS


[16, 2010] Node OS TinyOS

Sensor Specifications Model, Range, Accuracy, Power


Soil sensor: Temperature, Moisture, Conductivity

Weather sensor: Temperature, Light Intensity, Relative

Humidity


Concepts used Neural Network & Fuzzy Logic

[18, 2010] Monitoring System Laptop

Node Platform IRIS Motes


[19, 2011] Microcontroller / Processor ARM Microcontroller system (LPC2148F model)





Number of nodes 20

Protocols/ Algorithms Location Routing Algorithm with Cluster Based

Flooding

Node OS PaRTiKle OS

Language used POSIX


Soil sensor: Temperature, Moisture

Weather sensor: Ambient Temperature, Relative

Humidity

[21, 2011] Monitoring System Monitoring System/PDA

Topology/ Architecture Dynamic Mesh Network Multi hop

Protocols/ Algorithms Beacon Only Period and Last Address Assignment

Algorithm

Node OS TinyOS

[23, 2011] Monitoring Object Waxberry

Location of Monitoring Greenhouse

Deployment type: Prototype

Deployment duration: 15 months

Topology/ Architecture Self-Organized Multi hop

Node Platform Telos B


Number of nodes 9

Node OS TinyOS-2.1.0

Routing Protocol FTSP (Robust, hHHigh Synchronization accuracy

achiever)

Data Aggregation Protocol CTP

Data Structure used Stack (FIFO)

Power supply Solar

Installation: Area Covered (size) 1500 m2


Weather sensor: Temperature, Humidity,

Photosensitivity, Light Intensity

Microcontroller IEEE 802.15.4/Zigbee Wireless Microcontroller

Processor ARM9

Framework used NAV (Advanced Vineyard Network)

Technology used GPRS

[26, 2011] Deployment type: Prototype


Weather sensor: Ambient Temperature, Relative

Humidity

Soil sensor: Moisture, Water level

Crop sensor: pH value

Communication Network Mesh Network

[27, 2011] Microcontroller AT91SAM7S256

Node OS JAVA Virtual Machine simpleRTJ (Multithreading &

Run-time Notifications)

Method used Efficient and Portable Reprogramming Method


Weather sensor: Temperature, Humidity, Light

Soil sensor: Water level

Technology used ZigBee

Simulator used SHAWN

[28, 2011] Location of Monitoring Vineyard





Weather sensor: Temperature, Humidity, Wind Speed

sensor, Wind Direction sensor, Solar Radiation

Precipitation sensor

Soil sensor: Soil Water potential

Crop sensor: Crop Evapotranspiration

Paradigm used Decision Support System

Concept used Fuzzy Controller

[29, 2011] Topology Tree

Architecture Multi hop

Number of nodes 20

Routing Protocol/ Algorithm EEZRP (Energy Efficient Zone based Routing

Protocol)

Dynamic Flood Routing using RELMA Localization

approach

Installation: Network size 100X100 m2

Simulation Environment C and statistical analysis

Sensing range 10m


Communication range 20m

[33, 2012] Framework used mKRISHI Sensor Network Platform

Routing Protocol Mesh Protocol

Instrument used Texas Instrument’s CC2530

Node OS TinyOS

Language used nesC



Weather sensor: Ambient Temperature, Humidity

Soil sensor: Temperature, Moisture

[34, 2012] Algorithm used MPPT (Maximum Power Point Tracking) Algorithm

Method used Perturb & Observe

Scheme used Energy Harvesting

Mode applied Heterogeneous


[36, 2013] Sensors used Chlorophyll (SPAD) meter and Optical Sensor

Greenseeker

Crop observed Wheat


Soil sensor: Water potential


[38, 2013] Concept used TSPN (Traveling Salesman Problem with

Neighborhoods)

Sensor used Minolta SPAD-502 Chlorophyll meter

[39, 2013] Terms used FM (Frequency Modulation), FDMA (Frequency

Division Multiple Access)


Weather sensor: Capacitive Relative Humidity

Communication range 50m

[40, 2013] Concept used Vegetation

Module used XBee

Sensing measurement RSSI (Received Signal Strength Indicator)

[41, 2013] System used (WUSA-CP) Wireless Underground Sensor-Aided

Centre Pivot system

Sensor used Wireless Underground Sensor




Table 2. Summarised Table of the Specifications in the Existing Techniques for Precision Agriculture using Wireless Sensor

Networks

Node platform Mica2

Node OS TinyOS


Soil sensor: Temperature, Moisture, Permittivity,

Salinity

Sensing measurement RSSI (Received Signal Strength Indicator)

[42, 2014] Sensor used Silicon Temperature Sensor ( TC1047A), SDI-12

sensor

Concept used Frost characterization

Term used CSMA-CA (Carrier Sense Multiple Access with

Collision Avoidance)

Microcontroller PIC12F683, ATmega128IV

Transceiver AVR AT86RF30

[45, 2014] Microcontroller ATmega16

Module RF

Sensors used LM-35DZ Soil Temperature sensor, Soil Moisture

sensor

Monitoring system LCD

[46, 2014] Crop Monitored Potato

Energy Efficient Method Energy Balancing & Conservation


Soil sensor: Temperature, Moisture, Humidity, PH

value

Topology/Architecture Grid

Algorithm used Hybrid Energy Efficiency Distributed Algorithm

Method used Recursive Converging Quartiles Method

Process used Clustering

Routing Algorithm Hierarchical Routing

Database used Postgres

[48, 2014] Proposed system Smart Environment Monitoring System

Strategy used Cognitive Path Planning

Energy Efficient Method Energy Consumption Reduction

Topology/Architecture Grid

[49, 2014] Types of Sensors: Sensed Parameters

Soil sensor: Temperature, Humidity, Soil Organic

Matter

Weather sensor: Light Intensity/ Illuminance, Wind

Speed sensor, Wind Direction sensor

Simulator used LabView

[50, 2014] Technology used Radio Frequency IDentification

Simulator used LabView

[51, 2014] Paradigm used Software Product Line

Power source used Solar Cell & Wind Power Generator

Types of Sensors used Digital & Analog Sensors

[52 and 53,

2014]

Types of Sensors used Multimedia Sensors

Process used Node Localization

Measurements performed Received Signal Strength & Time of Arrival

Number of Nodes 25

Installation: Area Covered (size) 150m X 150m




3. PROBLEM FORMULATION

The intention of this research is to develop a Wireless Sensor

Network system to monitor and control of various ecological

parameters coupled with Precision Agriculture. The WSN

routing protocol could be designed in such a way, to exhibit the

following criteria:

1. The protocol will be scalable and function efficiently

for networks of any size.

2. The failure of some nodes will not affect the working

of the protocol.

3. The protocol will be energy efficient and will extend

the network lifetime.

4. The protocol will be computationally simple and easy

to implement.

5. The protocol will be Quality of Service (QoS) based.

4. RESEARCH OBJECTIVES

The research objectives are the following:

1. To study various Wireless Sensor Network techniques

and their applications in Precision Agriculture.

2. To examine the major shortcomings of existing WSN

approaches. The network lifetime and energy

efficiency are the chief issues to be considered.

3. To propose a new WSN technique for improving the

above issues.

4. To demonstrate the applicability of the proposed

technique in the field of Precision Agriculture.

5. To perform the simulation study of the proposed

technique and analyze the results under varying

simulation scenarios using simulation tool.

5. RESEARCH METHODOLOGY

The various phases of the research activity are described as

below:

Phase 1: The techniques, benefits and limitations of the existing

approaches in Precision Agriculture using Wireless Sensor

Networks are to be reviewed.

Phase 2: On the basis of Phase 1, the major shortcomings will

be addressed, and a new WSN technique will be proposed to

improve those limitations.

Phase 3: The proposed WSN technique will be demonstrated

for its applicability in the field of Precision Agriculture.

Phase 4: Then, the performance of the proposed technique will

be evaluated through simulation studies to meet the design

objectives.

Phase 5: The conclusions and recommendations will be given

and report writing will be done.

Figure 2. Architecture of Proposed System

6. PROPOSED TOOL

MATLAB (Matrix Laboratory) [63, 64] is the “Language of

Technical Computing.

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